Manu R.'s Linkedin Analytics

Are Emotional Signals Really the New Drivers of Consumer Behavior in the Auto Industry? Despite what traditional data suggests, the stability in the auto industry may be more fragile than it appears. While prices have remained steady, there’s an emotional undercurrent shifting consumer behavior—and it’s becoming a more significant driver of decisions than ever before. In my recent article, I explore how emotional fatigue, anxiety, and uncertainty are reshaping consumer behavior in ways that traditional economic models can’t fully capture. Consumers aren’t just holding off on purchases because of pricing—they’re overwhelmed by the broader economic volatility, which is deeply affecting their decision-making. What’s more, price stability isn’t necessarily a sign of market resilience. We’re witnessing emotional restraint, where consumers are being influenced not just by affordability but by emotional signals—things like inflation, job insecurity, and general economic fatigue—that are telling them to wait. Automakers like Ford and GM are clearly responding to these emotional limits, as shown by their decision to limit price hikes despite tariff pressures. We’ve reached a point where emotional intelligence has to be embedded into business forecasting and strategy. Relying on traditional metrics and economic data alone won’t be enough to navigate these times. Businesses need to start thinking about emotion-aware forecasting, where they track not just market trends, but the emotional dynamics shaping consumer decisions. What’s your take? 📌 How can companies adapt their strategies to account for emotional signals in consumer behavior? 📌 Is there a more effective way to integrate emotional intelligence into decision-making? I’d love to hear your thoughts and see how others are navigating these shifts in their industries. Read the full article here: https://lnkd.in/dtSjSKrp #ConsumerBehavior #EmotionalIntelligence #AutoIndustry #MarketDynamics #BusinessStrategy #EmotionalForecasting #AI

We used to worry that AI would become too smart to control. Now we’re seeing what happens when humans build powerful systems they can’t govern. I wrote about how Superintelligence (2014) and Supremacy (2024)—two books published ten years apart—show our biggest risk was never the machine. It was us:

We didn’t fail to solve the AI alignment problem. We skipped over it. When I wrote “The AI Control Problem Was Never Technical — It Was Human,” I was reflecting on what two books — Superintelligence (2014) and Supremacy (2024) — reveal about where our attention has gone in the AI conversation. Bostrom asked, What if AGI becomes too smart to control? Olson, more urgently, shows: We aren’t even aligned enough to control ourselves. We have powerful models. What we lack are: 📌 Clear governance structures 📌 Transparent decision-making 📌 Institutional coordination The irony? The original “control problem” wasn’t wrong. It was just aimed at the wrong target. The control problem isn’t artificial. It’s human. Curious to hear from others: In your field or organization, where are you seeing signs of this misalignment play out? See article here:

Hawaii just passed the first climate resilience tourist tax in the U.S. It’s bold, it’s overdue — and it might only scratch the surface of what’s possible. What if climate policy could think ahead? What if AI helped us decide where to invest, when to act, and how to measure what’s working — before disaster strikes? This article explores how Hawaii’s Green Fee could be the beginning of something bigger: climate intelligence that’s dynamic, transparent, and precision-targeted: https://lnkd.in/g4VFXB48

🌍 From Policy to Possibility: What Happens After Hawaii’s Green Fee? On Monday, I wrote about Hawaii’s new climate resilience tax, and what it could signal for the future. Today, I want to pose a question: "What if every policy decision had an intelligent feedback loop?" A system where we don’t just respond to environmental change—we predict it, simulate it, and finance solutions ahead of time. Hawaii’s Green Fee is a milestone. But with the right tech stack—AI models, dynamic pricing, real-time environmental data—it could become a prototype for living, learning policy. ➡️ Resilience isn’t just about reacting faster. It’s about designing systems that adapt as conditions shift. We're entering an era where machine learning could become public infrastructure, quietly optimizing where dollars go, when alerts trigger, and how risks are managed. It’s early, but the signal is clear: climate policy and climate intelligence are converging. And that might be one of the most hopeful trends we’ve seen in a long time. 📖 https://lnkd.in/g4VFXB48 #ClimateResilience #AIForGood #PolicyInnovation #Hawaii #FutureOfGovernance